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Record W3036094632 · doi:10.1080/13218719.2020.1767719

Critical considerations in the development and interpretation of common risk language

2020· article· en· W3036094632 on OpenAlexaff
Neil R. Hogan

Bibliographic record

VenuePsychiatry Psychology and Law · 2020
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsRisk analysis (engineering)Interpretation (philosophy)Risk assessmentCriminal justiceComputer scienceConceptual frameworkEconomic JusticeManagement scienceEngineering ethicsPsychologyComputer securitySociologyBusinessPolitical scienceEngineeringCriminologyLawSocial science

Abstract

fetched live from OpenAlex

Existing risk communication procedures are marred by various well-documented problems and inconsistencies. The Council of State Governments' Justice Center (United States) developed a five-level system for risk and needs communication, to standardize these procedures and to provide a common risk language. Introduction of a common language could constitute a dramatic shift in criminal justice processes, with wide-ranging impacts. This article provides a critical review of the system and its suitability for application to various risk assessment functions. Issues discussed include: applicability to specialist and generalist offending behavior, the characteristics of suitable instruments, statistical and conceptual priorities, barriers to precision in language, and conceptual issues related to changes in risk level. A thorough understanding of each of these issues is necessary to apply the system to new contexts and populations, and facilitate straightforward and precise risk communication. Absent further elaboration of the system, many problems with risk communication will persist.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.721
Threshold uncertainty score0.717

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.028
GPT teacher head0.355
Teacher spread0.327 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations13
Published2020
Admission routes1
Has abstractyes

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